SOTAVerified

Optical Character Recognition (OCR)

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)

Papers

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Showing 1126–1150 of 1209 papers

TitleStatusHype
Topic Stability over Noisy Sources—0
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text RecognitionCode4
SAHSOH@QALB-2015 Shared Task: A Rule-Based Correction Method of Common Arabic Native and Non-Native Speakers' Errors—0
A Linked Data Model for Multimodal Sentiment and Emotion Analysis—0
A preliminary study on similarity-preserving digital book identifiers—0
TECHLIMED@QALB-Shared Task 2015: a hybrid Arabic Error Correction System—0
License Plate Recognition System Based on Color Coding Of License Plates—0
Boosting Optical Character Recognition: A Super-Resolution Approach—0
Automated Translation of a Literary Work: A Pilot Study—0
Unsupervised Code-Switching for Multilingual Historical Document Transcription—0
Squibs: Spelling Error Patterns in Brazilian Portuguese—0
Regularization and Kernelization of the Maximin Correlation Approach—0
A survey of modern optical character recognition techniques—0
A Study of Sindhi Related and Arabic Script Adapted languages Recognition—0
Learning Multiple Tasks in Parallel with a Shared Annotator—0
Efficient Media Retrieval from Non-Cooperative Queries—0
Optical Character Recognition, Using K-Nearest Neighbors—0
OCR and Automated Translation for the Navigation of non-English Handsets: A Feasibility Study with Arabic—0
A random forest system combination approach for error detection in digital dictionaries—0
Improve CAPTCHA's Security Using Gaussian Blur Filter—0
CMUQ@QALB-2014: An SMT-based System for Automatic Arabic Error Correction—0
TECHLIMED system description for the Shared Task on Automatic Arabic Error Correction—0
Autocorrection of arabic common errors for large text corpus—0
Balanced Korean Word Spacing with Structural SVM—0
Bypassing Captcha By Machine A Proof For Passing The Turing Test—0
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All datasetsBenchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical StudyVideoDB's OCR Benchmark Public CollectionFSNS - TestI2L-140KSUTim2latex-100k

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DTrOCRAccuracy (%)89.6—Unverified
2DTrOCR 105MAccuracy (%)89.6—Unverified
3MaskOCR-LAccuracy (%)82.6—Unverified
4TransOCRAccuracy (%)72.8—Unverified
5SRNAccuracy (%)65—Unverified
6MORANAccuracy (%)64.3—Unverified
7SEEDAccuracy (%)61.2—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4oAverage Accuracy76.22—Unverified
2Gemini-1.5 ProAverage Accuracy76.13—Unverified
3Claude-3 SonnetAverage Accuracy67.71—Unverified
4RapidOCRAverage Accuracy56.98—Unverified
5EasyOCRAverage Accuracy49.3—Unverified
#ModelMetricClaimedVerifiedStatus
1STREETSequence error27.54—Unverified
2SEESequence error22—Unverified
3AttentionOCR_Inception-resnet-v2_LocationSequence error15.8—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-NOPOOLBLEU89.09—Unverified
2I2L-STRIPSBLEU89—Unverified
#ModelMetricClaimedVerifiedStatus
1TesseractCharacter Error Rate (CER)0.08—Unverified
2EasyOCRCharacter Error Rate (CER)0.07—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-STRIPSBLEU88.86—Unverified